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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| NLP, CCS | 2018-06-10 12:10 |
Kyoto | Kyoto Terrsa | A study on modeling accelerator pedal with hysteresis Kenta onuma, Yoshikazu Yamanaka (Utsunomiya Univ), Hideki Takamatsu (Toyota Motor), katsutoshi Yoshida (Utsunomiya Univ)  NLP2018-45 CCS2018-18 |
Accelerator pedals which make it possible to reduce stress and fatigue of drivers are required. For this purpose, it is ... [more] | NLP2018-45 CCS2018-18 pp.97-101 |
| NLP | 2018-04-27 16:35 |
Kumamoto | Kumaoto Univ. | A Particle Swarm Optimizer Based on Periodically Swiched Particle Networks Santana Sato, Hidehiro Nakano, Arata Miyauchi (Tokyo City Univ.) NLP2018-27 |
In this paper, we propose a method to periodically switch the couplings between particles in Particle Swarm Optimization... [more] | NLP2018-27 pp.133-137 |
| PRMU, BioX | 2018-03-19 10:25 |
Tokyo | Proposal of an iris code generation method depending on the traits of the iris Kota Oishi, Hiroyuki Yoshimura (Chiba Univ.) BioX2017-58 PRMU2017-194 |
(To be available after the conference date) [more] | BioX2017-58 PRMU2017-194 pp.133-138 |
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| EA | 2018-02-16 14:10 |
Hiroshima | Pref. Univ. Hiroshima | 1ch Acoustic Distance Measurement Method Based on Phase Interference Between Transmitted and Reflected Waves Using Particle Swarm Optimization Changryung Song, Toshihiro Shinohara, Testuji Uebo, Noboru Nakasako (Kindai Univ) EA2017-101 |
The distance to a target is basic information in many engineering fields. As the distance estimation using acoustic sign... [more] | EA2017-101 pp.47-52 |
| EE | 2018-01-29 13:15 |
Oita | Satellite Campus Oita | [Poster Presentation]
Influence On Partial Shadows In Photovoltaic Power Generation System And Its Countermeasure
-- Maximum Power Point Tracking Control Using Particle Swarm Optimization -- Yuichi Nagatsu, Genki Hara, Terukazu Sato, Kimihiro Nishizima (Oita Univ) EE2017-58 |
This paper considers the usefulness of PSO (particle swarm optimization) in MPPT (maximum power point tracking) control.... [more] | EE2017-58 pp.93-97 |
| MBE, NC, NLP (Joint) |
2018-01-27 09:55 |
Fukuoka | Kyushu Institute of Technology | NLP2017-94 |
In the update rule of the velocity for PSO, random numbers are stochastically independent of dimensional components. Thi... [more] | NLP2017-94 pp.45-50 |
| MBE, NC, NLP (Joint) |
2018-01-27 13:35 |
Fukuoka | Kyushu Institute of Technology | The Search Feature of Particle Swarm Optimizer with Sensors in Dynamic Environment Hiroshi Sho (KIT) NC2017-63 |
In order to perform the search of particle swarm optimizer under dynamic environment, as a previous study, author has pr... [more] | NC2017-63 pp.77-82 |
| EMT, IEE-EMT | 2017-11-11 10:00 |
Yamagata | Tendo Hotel (Tendo, Yamagata) | Placement Design of Wireless Base Stations in Indoor Environment Using Particle Swarm Optimization Takahiro Hashimoto, Takayuki Nakanishi, Yoshio Inasawa, Naofumi Yoneda (Mitsubishi Electric Corp.) EMT2017-71 |
In order to introduce wireless devices efficiently in indoor environment, we propose an placement optimization algorithm... [more] | EMT2017-71 pp.259-262 |
| NLP | 2017-11-05 13:10 |
Miyagi | Research Institute of Electrical Communication Tohoku University | Analysis of solution search procedure of particle swarm optimization Seinosuke Ishikawa, Kenya Jin'no (NIT) NLP2017-65 |
In order to clarify the solution search procedure of the particle swarm optimization, we have proposed a deterministic p... [more] | NLP2017-65 pp.1-6 |
| NLP | 2017-11-05 15:05 |
Miyagi | Research Institute of Electrical Communication Tohoku University | On a TSP solver based on PSO Jun Kiyama, Kenya Jin'no (NIT) NLP2017-69 |
Insertion-based particle swarm optimization strategy (abbr．IPSO) is a traveling salesperson problem (abbr．TSP) solver wh... [more] | NLP2017-69 pp.25-28 |
| IE, ITE-ME, ITE-AIT [detail] | 2017-10-05 13:50 |
Nagasaki | Estimation of Facets of a Point Cloud Obtained from a Gallery Wall Using Multi-Dimensional Particle Swarm Optimization Yuto Matsuura, Shun Matsukawa, Ken-ichi Itakura (Muroran-IT), Akira Hayano (JAEA), Yukinori Suzuki (Muroran-IT) IE2017-49 |
It is necessary to identify discontinuities of a gallery wall to evaluate the stability of the gallery structure. A poin... [more] | IE2017-49 pp.13-18 |
|
| CQ (2nd) |
2017-08-27 13:50 |
Saitama | Nippon Institute of Technology | [Poster Presentation]
Performance of a Stereophonic Acoustic Echo Canceller Based on the Adaptive PSO Algorithm for Environmental Change Yosuke Yoneda, Masanori Kimoto (NIT) |
In stereophonic acoustic echo canceller (SAEC), a unique probrem called mis-adjustment of coefficent occurs due to the i... [more] | |
| NLP | 2017-07-14 14:50 |
Okinawa | Miyako Island Marine Terminal | Multi-objective Particle Swarm Optimizer Networks with Tree Topology Kyosuke Miyano, Hidehiro Nakano, Arata Miyauchi (Tokyo City Univ.) NLP2017-47 |
In this paper, we consider island-model multi-objective particle swarm optimization (IMOPSO) in which plural sub-swarms ... [more] | NLP2017-47 pp.103-106 |
| NLP | 2017-05-11 16:25 |
Okayama | Okayama University of Science | I-PD Controller Design Using Particle Swarm Optimizer
-- Settling Time Minimization Under Constraint of the Gain Crossover Frequency -- Yuzo Ohta (Kobe Univ.) NLP2017-14 |
In this paper, we consider the parameter tuning of I-PD controller which achieves minimum settling time control under th... [more] | NLP2017-14 pp.69-72 |
| NLP | 2017-03-14 10:00 |
Aomori | Nebuta Museum Warasse | Search Capability of DPSO with Dynamically Varying Gain-Parameter Nobuaki Hashimoto, Masato Kaneko, Toshiya Iwai (Nihon Univ.) NLP2016-106 |
Discrete Particle Swarm Optimization(DPSO) is a metaheuristics that is improved to apply PSO to the discrete optimizatio... [more] | NLP2016-106 pp.1-6 |
| NLP | 2017-03-14 10:25 |
Aomori | Nebuta Museum Warasse | Search Capability of Random Search PSO with Linked Random Update Kouhei Sakayori, Masato Kaneko, Toshiya Iwai (Nihon Univ.) NLP2016-107 |
Particle Swarm Optimization (PSO) is a metaheuristics using the swarm intelligence. Although PSO is usually applied to t... [more] | NLP2016-107 pp.7-12 |
| NLP | 2016-12-13 10:30 |
Aichi | Chukyo Univ. | Particle Swarm Optimization with Refractory Period of Particle Velocity Update Yuki Nagano, Hideharu Toda (Chukyo Univ.), Masatoshi Sato (Tokyo Metropolitan Univ.), Hisashi Aomori (Chukyo Univ.) NLP2016-94 |
Particle Swarm Optimization (PSO) is one of the metaheuristics where each particles in a swarm searches an optimal solut... [more] | NLP2016-94 pp.55-59 |
| MSS, CAS, IPSJ-AL [detail] | 2016-11-24 14:15 |
Hyogo | Kobe Institute of Computing | Effects of Time-Varying Parameters in Particle Swarm Optimization of Multiple Swarms under Search-Time Constraints Yuya Asato (Univ. of the Ryukyus), Takeshi Tengan (Meio Univ.), Morikazu Nakamura (Univ. of the Ryukyus) CAS2016-65 MSS2016-45 |
This paper proposes a time-varying parameter setting method for particle swarm optimization of multiple swarms under sea... [more] | CAS2016-65 MSS2016-45 pp.43-48 |
| MBE, NC (Joint) |
2016-11-19 14:35 |
Miyagi | Tohoku University | Multiple Particle Swarm Optimizers Based on Information Sharing Hiroshi Sho (KyuTech) NC2016-37 |
In order to improve the search performance of multiple particle swarm optimizers, this paper proposes multiple particle ... [more] | NC2016-37 pp.27-32 |
| CAS, NLP | 2016-10-27 09:55 |
Tokyo | Parameter Optimization for Power Line Communications Considering Operational Status of Electrical Appliances Shunsuke Yasui, Takeshi Kamio, Ena Kono, Hisato Fujisaka (Hiroshima City Univ.) CAS2016-39 NLP2016-65 |
Power line communication (PLC) is considered as one of communication systems to support a smart grid. Especially, PLC ha... [more] | CAS2016-39 NLP2016-65 pp.5-10 |
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